With the unprecedented development of smart vehicles and roadside units equipped with wireless connectivity, the transportation system is undergoing revolutionary changes in the past decade or two. Bearing safety and efficiency as the utmost objectives, the vehicular environments are witnessing explosive increase of various sensors onboard vehicles and equipped at transportation infrastructures. On the one hand, these sensors are destined to be wirelessly connected to provide more comprehensive situational awareness for transportation purposes. On the other hand, the abundance of sensor data of the environment can potentially shed light on the channel propagation characteristics that lie at the core of any communications system design. The integrated sensing and communications (ISACs) is henceforth both necessary and natural in vehicular communications networks (VCN). Different from existing ISAC works that target generic environments but are limited to dual-function radar-communications (DFRC), in this article we focus on transportation scenarios and applications but take a wholistic view of ISAC possibilities. First, we argue that, even though many sensors in transportation settings are nonradio-frequency (RF)-based, functional ISAC (fISAC) is feasible and necessary, in both communication-centric (CC) or sensing-centric (SC) modes. To facilitate this, the concept of synesthesia is introduced to ISAC to accommodate “machine senses” in the RF and non-RF formats. We then zoom in to RF-based sensors and propose the so-termed signaling ISAC (sISAC), with either unified-hardware (UH) or separate-hardware (SH) platforms, and delineate the unique issues arising in transportation settings. Several transportation-specific case studies are included to demonstrate these various ISAC regimes. Toward the end, the relationships of these ISAC subcategories are discussed with a roadmap laid out.

The virtual synchronous generator (VSG) improves the robustness of the inverter-interfaced distributed generator (IIDG) against instability by introducing a virtual inertia. However, the transient response of the active power and the angular frequency conflict with each other for the IIDG with fixed inertia control. It is necessary to adopt adaptive control to improve overall performances of power and frequency as the operating condition changes. This paper analyzes the impact of the inertia on power and angular frequency. A dual-adaptivity inertia control strategy is proposed to offer a responsive and stable frequency support and also achieve the balance between power regulation and frequency regulation according to different operating conditions. The principle of parameter design is given to obtain the range of adaptivity. Quantitative assessment considering the cumulative effect of the output deviation and its duration is also presented to evaluate the proposed strategy intuitively. The strategy is further verified based on PSCAD/EMTDC and a hardware-in-loop experiment platform based on RTDS. Results confirm that the proposed strategy not only achieves rapid frequency response with slight dynamic deviations under disturbances but also strikes a balance between the frequency and power and leads to improved overall control.

Vehicle localization and mobility tracking are important tasks in autonomous driving. Traditional methods either have insufficient accuracy or rely on additional facilities to reach the desired accuracy for autonomous driving. In this paper, a multi-sensor multi-vehicle localization and mobility tracking framework is developed for autonomous vehicles equipped with GPS, inertial measurement unit (IMU), and an integrated sensing system. Our algorithm fuse the information from local onboard sensors as well as the observations of other vehicles or existing intelligent transportation system infrastructure such as road side units (RSU) to improve the precision and stability of localization and mobility tracking. Specifically, this framework incorporates the dynamic model of vehicles to achieve better localization and tracking performance. The communication delays during the information sharing process are explicitly taken into account in our algorithm development. Simulations manifest that not only the accuracy of localization and mobility tracking could be greatly enhanced in general, but also the robustness can be guaranteed under circumstances where traditional localization and tracking devices fail.

Global positioning system (GPS) spoofing attack (GSA) has been shown to be one of the most imminent threats to almost all cyber-physical systems incorporated with the civilian GPS signal. Specifically, for our current agenda of the modernization of the power grid, this may greatly jeopardize the benefits provided by the pervasively installed phasor measurement units (PMUs). In this paper, we consider the case where synchrophasor data from PMUs are compromised due to the presence of a single GSA, and show that it can be corrected by signal processing techniques. In particular, we introduce a statistical model for synchrophasor-based power system state estimation (SE), and then derive the spoofing-matched algorithms for synchrophasor data correction against GSA. Different testing scenarios in IEEE 14-, 30-, 57-, 118-bus systems are simulated to show the proposed algorithms' performance on GSA detection and SE. Numerical results demonstrate that our proposed algorithms can consistently locate and correct the spoofed synchrophasor data with good accuracy as long as the system observability is satisfied. The accuracy of SE is significantly improved compared with the traditional weighted least square method and approaches the performance under the Genie-aided method.

In this letter, we introduce integrated sensing and communications (ISAC) to multi-hop vehicle-to-vehicle (V2V) networks and aim to augment end-to-end information delivery. As ISAC does not automatically guarantee excellence, we therefore carefully formulate two types of problems to jointly optimize power allocation and link selection. Via the proposed efficient solvers, either the power consumption or the outage probability can be significantly lowered than the traditional non-sensing-aided V2V. Aside from the sensed positioning information, the sensed velocity information is also exploited in V2V networking to help combat the Doppler shifts. Thanks to our optimum-ensuring scheme and merit-distilling analysis, this preliminary study is promising to provide a valuable guideline for the deployment of ISAC in future vehicular networks.

In autonomous driving, environment perception is the fundamental task for intelligent vehicles which provides the necessary environment information for other applications. The main issues in existing environment perception can be categorized into two aspects. On the one hand, all sensors are prone to measurement errors and failures. On the other hand, in complex driving environments, vehicles may encounter a variety of blind spots caused by vehicle occlusions, overlaps, and harsh weather conditions, which will cause sensors to experience low-quality data or to miss crucial environmental information. To cope with these issues, a multivehicle and multisensor (MVMS) cooperative perception method is presented to construct the occupancy grid map (OGM) of vehicles in a global view for the environment perception of autonomous driving. Distinct from existing environment perception methods, our proposed MVMS-OGM not only provides continuous geographical information but also captures and fuses continuous information with soft occupancy probabilities, resulting in more comprehensive and raw environmental information. Simulations and real-world experiments demonstrate that the proposed approach not only expands the perception range in comparison with single-vehicle sensing but also better captures the uncertainty of sensor data by fusing the occupancy probabilities with soft information.